Overview

Dataset statistics

Number of variables9
Number of observations932
Missing cells50
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.7 KiB
Average record size in memory72.1 B

Variable types

Numeric7
Categorical2

Alerts

Builtup_area is highly overall correlated with Carpet_areaHigh correlation
Carpet_area is highly overall correlated with Builtup_areaHigh correlation
Hospital_dist is highly overall correlated with Market_dist and 1 other fieldsHigh correlation
Market_dist is highly overall correlated with Hospital_distHigh correlation
Taxi_dist is highly overall correlated with Hospital_distHigh correlation
Taxi_dist has 13 (1.4%) missing valuesMissing
Market_dist has 13 (1.4%) missing valuesMissing
Builtup_area has 15 (1.6%) missing valuesMissing
Carpet_area is highly skewed (γ1 = 25.95686468)Skewed
Price_house is highly skewed (γ1 = 25.27467092)Skewed

Reproduction

Analysis started2026-02-22 15:50:20.852996
Analysis finished2026-02-22 15:50:28.078152
Duration7.23 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Taxi_dist
Real number (ℝ)

High correlation  Missing 

Distinct884
Distinct (%)96.2%
Missing13
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean8229.728
Minimum146
Maximum20662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2026-02-22T20:50:28.159557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum146
5-th percentile4111.8
Q16476
median8230
Q39937
95-th percentile12386.3
Maximum20662
Range20516
Interquartile range (IQR)3461

Descriptive statistics

Standard deviation2561.985
Coefficient of variation (CV)0.31130859
Kurtosis0.4425674
Mean8229.728
Median Absolute Deviation (MAD)1741
Skewness0.14280014
Sum7563120
Variance6563767.2
MonotonicityNot monotonic
2026-02-22T20:50:28.288737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72142
 
0.2%
77392
 
0.2%
127942
 
0.2%
85102
 
0.2%
48462
 
0.2%
106622
 
0.2%
84752
 
0.2%
91542
 
0.2%
80852
 
0.2%
49172
 
0.2%
Other values (874)899
96.5%
(Missing)13
 
1.4%
ValueCountFrequency (%)
1461
0.1%
6041
0.1%
12001
0.1%
12411
0.1%
16371
0.1%
16481
0.1%
18681
0.1%
20171
0.1%
22221
0.1%
23141
0.1%
ValueCountFrequency (%)
206621
0.1%
168501
0.1%
162331
0.1%
155221
0.1%
153211
0.1%
150821
0.1%
146371
0.1%
144701
0.1%
143061
0.1%
140051
0.1%

Market_dist
Real number (ℝ)

High correlation  Missing 

Distinct866
Distinct (%)94.2%
Missing13
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean11018.753
Minimum1666
Maximum20945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2026-02-22T20:50:28.410960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1666
5-th percentile6713.3
Q19354.5
median11161
Q312670.5
95-th percentile14999.9
Maximum20945
Range19279
Interquartile range (IQR)3316

Descriptive statistics

Standard deviation2543.9206
Coefficient of variation (CV)0.23087191
Kurtosis0.050138569
Mean11018.753
Median Absolute Deviation (MAD)1687
Skewness-0.037130815
Sum10126234
Variance6471532
MonotonicityNot monotonic
2026-02-22T20:50:28.539799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114653
 
0.3%
116732
 
0.2%
151272
 
0.2%
133332
 
0.2%
107812
 
0.2%
115622
 
0.2%
116222
 
0.2%
126562
 
0.2%
101342
 
0.2%
115892
 
0.2%
Other values (856)898
96.4%
(Missing)13
 
1.4%
ValueCountFrequency (%)
16661
0.1%
44021
0.1%
45741
0.1%
46441
0.1%
49501
0.1%
51341
0.1%
51421
0.1%
51771
0.1%
52501
0.1%
52761
0.1%
ValueCountFrequency (%)
209451
0.1%
182811
0.1%
179581
0.1%
175521
0.1%
175411
0.1%
174861
0.1%
172271
0.1%
171111
0.1%
171011
0.1%
170401
0.1%

Hospital_dist
Real number (ℝ)

High correlation 

Distinct895
Distinct (%)96.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13072.092
Minimum3227
Maximum23294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2026-02-22T20:50:28.673738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3227
5-th percentile8731.5
Q111301.5
median13163
Q314817
95-th percentile17048
Maximum23294
Range20067
Interquartile range (IQR)3515.5

Descriptive statistics

Standard deviation2586.4562
Coefficient of variation (CV)0.19786092
Kurtosis0.27178913
Mean13072.092
Median Absolute Deviation (MAD)1742
Skewness-0.060977825
Sum12170118
Variance6689755.5
MonotonicityNot monotonic
2026-02-22T20:50:28.812510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154912
 
0.2%
126942
 
0.2%
111702
 
0.2%
144792
 
0.2%
158452
 
0.2%
130162
 
0.2%
157212
 
0.2%
144052
 
0.2%
124542
 
0.2%
122672
 
0.2%
Other values (885)911
97.7%
ValueCountFrequency (%)
32271
0.1%
49221
0.1%
54461
0.1%
59131
0.1%
63161
0.1%
64221
0.1%
65831
0.1%
67641
0.1%
68081
0.1%
72511
0.1%
ValueCountFrequency (%)
232941
0.1%
224071
0.1%
202631
0.1%
200761
0.1%
200461
0.1%
196171
0.1%
194971
0.1%
190461
0.1%
190141
0.1%
188361
0.1%

Carpet_area
Real number (ℝ)

High correlation  Skewed 

Distinct595
Distinct (%)64.4%
Missing8
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1511.8626
Minimum775
Maximum24300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2026-02-22T20:50:28.949874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum775
5-th percentile1079.05
Q11318
median1480.5
Q31655
95-th percentile1908.85
Maximum24300
Range23525
Interquartile range (IQR)337

Descriptive statistics

Standard deviation790.96966
Coefficient of variation (CV)0.52317564
Kurtosis748.32524
Mean1511.8626
Median Absolute Deviation (MAD)167.5
Skewness25.956865
Sum1396961
Variance625633
MonotonicityNot monotonic
2026-02-22T20:50:29.095789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14395
 
0.5%
15145
 
0.5%
14585
 
0.5%
15395
 
0.5%
14405
 
0.5%
15135
 
0.5%
12504
 
0.4%
14624
 
0.4%
11744
 
0.4%
16094
 
0.4%
Other values (585)878
94.2%
(Missing)8
 
0.9%
ValueCountFrequency (%)
7751
0.1%
7801
0.1%
8541
0.1%
8691
0.1%
8911
0.1%
8961
0.1%
9022
0.2%
9131
0.1%
9191
0.1%
9322
0.2%
ValueCountFrequency (%)
243001
0.1%
22291
0.1%
22151
0.1%
22141
0.1%
21691
0.1%
20671
0.1%
20631
0.1%
20491
0.1%
20442
0.2%
20261
0.1%

Builtup_area
Real number (ℝ)

High correlation  Missing 

Distinct626
Distinct (%)68.3%
Missing15
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1794.9248
Minimum932
Maximum12730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2026-02-22T20:50:29.226298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum932
5-th percentile1298.4
Q11583
median1774
Q31982
95-th percentile2280.8
Maximum12730
Range11798
Interquartile range (IQR)399

Descriptive statistics

Standard deviation468.15946
Coefficient of variation (CV)0.260824
Kurtosis324.52653
Mean1794.9248
Median Absolute Deviation (MAD)200
Skewness13.919406
Sum1645946
Variance219173.28
MonotonicityNot monotonic
2026-02-22T20:50:29.520492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18585
 
0.5%
16484
 
0.4%
18694
 
0.4%
18204
 
0.4%
17464
 
0.4%
20654
 
0.4%
17334
 
0.4%
17344
 
0.4%
19434
 
0.4%
22624
 
0.4%
Other values (616)876
94.0%
(Missing)15
 
1.6%
ValueCountFrequency (%)
9321
0.1%
9511
0.1%
10181
0.1%
10501
0.1%
10591
0.1%
10731
0.1%
10871
0.1%
10931
0.1%
10991
0.1%
11061
0.1%
ValueCountFrequency (%)
127301
0.1%
26671
0.1%
26471
0.1%
26171
0.1%
24931
0.1%
24921
0.1%
24741
0.1%
24651
0.1%
24361
0.1%
24201
0.1%

Parking_type
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
Open
372 
Not Provided
227 
Covered
188 
No Parking
145 

Length

Max length12
Median length10
Mean length7.4871245
Min length4

Characters and Unicode

Total characters6978
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen
2nd rowNot Provided
3rd rowNot Provided
4th rowCovered
5th rowNot Provided

Common Values

ValueCountFrequency (%)
Open372
39.9%
Not Provided227
24.4%
Covered188
20.2%
No Parking145
 
15.6%

Length

2026-02-22T20:50:29.644848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-22T20:50:29.724805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
open372
28.5%
not227
17.4%
provided227
17.4%
covered188
14.4%
no145
 
11.1%
parking145
 
11.1%

Most occurring characters

ValueCountFrequency (%)
e975
14.0%
o787
11.3%
d642
9.2%
r560
 
8.0%
n517
 
7.4%
v415
 
5.9%
O372
 
5.3%
N372
 
5.3%
p372
 
5.3%
372
 
5.3%
Other values (7)1594
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)6978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e975
14.0%
o787
11.3%
d642
9.2%
r560
 
8.0%
n517
 
7.4%
v415
 
5.9%
O372
 
5.3%
N372
 
5.3%
p372
 
5.3%
372
 
5.3%
Other values (7)1594
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e975
14.0%
o787
11.3%
d642
9.2%
r560
 
8.0%
n517
 
7.4%
v415
 
5.9%
O372
 
5.3%
N372
 
5.3%
p372
 
5.3%
372
 
5.3%
Other values (7)1594
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e975
14.0%
o787
11.3%
d642
9.2%
r560
 
8.0%
n517
 
7.4%
v415
 
5.9%
O372
 
5.3%
N372
 
5.3%
p372
 
5.3%
372
 
5.3%
Other values (7)1594
22.8%

City_type
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
CAT B
365 
CAT A
329 
CAT C
238 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4660
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAT B
2nd rowCAT B
3rd rowCAT A
4th rowCAT B
5th rowCAT B

Common Values

ValueCountFrequency (%)
CAT B365
39.2%
CAT A329
35.3%
CAT C238
25.5%

Length

2026-02-22T20:50:29.825318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-22T20:50:29.887657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cat932
50.0%
b365
 
19.6%
a329
 
17.7%
c238
 
12.8%

Most occurring characters

ValueCountFrequency (%)
A1261
27.1%
C1170
25.1%
T932
20.0%
932
20.0%
B365
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1261
27.1%
C1170
25.1%
T932
20.0%
932
20.0%
B365
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1261
27.1%
C1170
25.1%
T932
20.0%
932
20.0%
B365
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1261
27.1%
C1170
25.1%
T932
20.0%
932
20.0%
B365
 
7.8%

Rainfall
Real number (ℝ)

Distinct131
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean785.5794
Minimum-110
Maximum1560
Zeros1
Zeros (%)0.1%
Negative1
Negative (%)0.1%
Memory size7.4 KiB
2026-02-22T20:50:29.986392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-110
5-th percentile360
Q1600
median780
Q3970
95-th percentile1220
Maximum1560
Range1670
Interquartile range (IQR)370

Descriptive statistics

Standard deviation265.54685
Coefficient of variation (CV)0.33802675
Kurtosis-0.17902532
Mean785.5794
Median Absolute Deviation (MAD)180
Skewness0.047163242
Sum732160
Variance70515.131
MonotonicityNot monotonic
2026-02-22T20:50:30.110886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67019
 
2.0%
79019
 
2.0%
76018
 
1.9%
68017
 
1.8%
77016
 
1.7%
66016
 
1.7%
73016
 
1.7%
70015
 
1.6%
86015
 
1.6%
90015
 
1.6%
Other values (121)766
82.2%
ValueCountFrequency (%)
-1101
 
0.1%
01
 
0.1%
701
 
0.1%
1001
 
0.1%
1201
 
0.1%
1301
 
0.1%
1401
 
0.1%
1601
 
0.1%
1901
 
0.1%
2103
0.3%
ValueCountFrequency (%)
15601
0.1%
15301
0.1%
14901
0.1%
14701
0.1%
14501
0.1%
14402
0.2%
14102
0.2%
14001
0.1%
13901
0.1%
13801
0.1%

Price_house
Real number (ℝ)

Skewed 

Distinct849
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6084695.3
Minimum30000
Maximum1.5 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2026-02-22T20:50:30.240341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30000
5-th percentile3307050
Q14658000
median5866000
Q37187250
95-th percentile8790800
Maximum1.5 × 108
Range1.4997 × 108
Interquartile range (IQR)2529250

Descriptive statistics

Standard deviation5025363.9
Coefficient of variation (CV)0.82590231
Kurtosis724.14922
Mean6084695.3
Median Absolute Deviation (MAD)1261000
Skewness25.274671
Sum5.670936 × 109
Variance2.5254282 × 1013
MonotonicityNot monotonic
2026-02-22T20:50:30.373590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63540003
 
0.3%
54590003
 
0.3%
56440002
 
0.2%
78130002
 
0.2%
62280002
 
0.2%
63660002
 
0.2%
76530002
 
0.2%
62180002
 
0.2%
78090002
 
0.2%
46050002
 
0.2%
Other values (839)910
97.6%
ValueCountFrequency (%)
300001
0.1%
14920001
0.1%
16370001
0.1%
17670001
0.1%
20270001
0.1%
20700001
0.1%
21300001
0.1%
21470001
0.1%
21650001
0.1%
21750001
0.1%
ValueCountFrequency (%)
1500000001
0.1%
116320001
0.1%
105150001
0.1%
102920001
0.1%
102310001
0.1%
101820001
0.1%
101780001
0.1%
101120001
0.1%
100900001
0.1%
99570001
0.1%

Interactions

2026-02-22T20:50:26.901360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:21.209380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:21.928438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:22.805038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:24.211353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:25.244334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:26.149823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:27.011021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:21.307487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:22.033726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:22.911929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:24.370239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:25.343896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:26.253257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:27.125763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:21.410990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:22.141035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:23.033019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:24.546984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:25.454284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:26.361530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:27.249989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-22T20:50:22.261191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:23.231590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:24.702343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:25.567242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:26.476370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:27.357255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:21.623743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:22.373944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:23.478562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:24.838798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:25.833591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:26.576138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:27.473771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:21.722263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:22.476757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:23.622216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:24.971170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:25.932336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:26.686624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:27.577871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:21.822027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:22.617137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:23.911436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:25.118665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:26.039471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T20:50:26.790913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-22T20:50:30.471054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Builtup_areaCarpet_areaCity_typeHospital_distMarket_distParking_typePrice_houseRainfallTaxi_dist
Builtup_area1.0000.9990.0000.009-0.0150.0000.089-0.0350.009
Carpet_area0.9991.0000.0000.011-0.0140.0330.095-0.0390.013
City_type0.0000.0001.0000.0000.0560.0000.0000.0000.000
Hospital_dist0.0090.0110.0001.0000.5880.0670.1380.0490.782
Market_dist-0.015-0.0140.0560.5881.0000.0300.1230.0630.418
Parking_type0.0000.0330.0000.0670.0301.0000.0320.0580.079
Price_house0.0890.0950.0000.1380.1230.0321.0000.0210.114
Rainfall-0.035-0.0390.0000.0490.0630.0580.0211.0000.008
Taxi_dist0.0090.0130.0000.7820.4180.0790.1140.0081.000

Missing values

2026-02-22T20:50:27.736714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-22T20:50:27.846766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-22T20:50:27.990791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Taxi_distMarket_distHospital_distCarpet_areaBuiltup_areaParking_typeCity_typeRainfallPrice_house
09796.05250.010703.01659.01961.0OpenCAT B5306649000
18294.08186.012694.01461.01752.0Not ProvidedCAT B2103982000
211001.014399.016991.01340.01609.0Not ProvidedCAT A7205401000
38301.011188.012289.01451.01748.0CoveredCAT B6205373000
410510.012629.013921.01770.02111.0Not ProvidedCAT B4504662000
56665.05142.09972.01442.01733.0OpenCAT B7604526000
613153.011869.017811.01542.01858.0No ParkingCAT A10307224000
75882.09948.013315.01261.01507.0OpenCAT C10203772000
87495.011589.013370.01090.01321.0Not ProvidedCAT B6804631000
98233.07067.011400.01030.01235.0OpenCAT C11304415000
Taxi_distMarket_distHospital_distCarpet_areaBuiltup_areaParking_typeCity_typeRainfallPrice_house
9229538.011551.012839.01655.01986.0CoveredCAT B11507743000
92311786.013969.015519.01156.01398.0OpenCAT A1409237000
9249615.07904.012521.01451.01734.0OpenCAT C6703488000
9257176.05779.012382.01539.01829.0OpenCAT B6504658000
92610915.017486.015964.01549.01851.0Not ProvidedCAT C12207062000
92712176.08518.015673.01582.01910.0CoveredCAT C10806639000
9287214.08717.010553.01387.01663.0OpenCAT A8508208000
9297423.011708.013220.01200.01436.0OpenCAT A10607644000
93015082.014700.019617.01299.01560.0OpenCAT B7709661000
9319297.012537.014418.01174.01429.0CoveredCAT C11105434000